Corrigendum: Megafaunal isotopes reveal role of increased ......important part in the late Quaternary megafaunal extinctions through alteration of environments such as rangelands,

NATURE ECOLOGY & EVOLUTION 1, 0199 (2017) | DOI: 10.1038/s41559-017-0199 | www.nature.com/natecolevol

ARTICLESNATURE ECOLOGY & EVOLUTION

The Supplementary Information files that were originally published with the Article were not the intended final files. Both files have been updated.

Corrigendum: Megafaunal isotopes reveal role of increased moisture on rangeland during late Pleistocene extinctionsM. Timothy Rabanus-Wallace, Matthew J. Wooller, Grant D. Zazula, Elen Shute, A. Hope Jahren, Pavel Kosintsev, James A. Burns, James Breen, Bastien Llamas and Alan Cooper

Nature Ecology & Evolution 1, 0125 (2017); published 18 April 2017; corrected 5 June 2017.

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http://dx.doi.org/10.1038/s41559-017-0199

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ARTICLESPUBLISHED: 18 APRIL 2017 | VOLUME: 1 | ARTICLE NUMBER: 0125

© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

NATURE ECOLOGY & EVOLUTION 1, 0125 (2017) | DOI: 10.1038/s41559-017-0125 | www.nature.com/natecolevol

Megafaunal isotopes reveal role of increased moisture on rangeland during late Pleistocene extinctionsM. Timothy Rabanus-Wallace1*, Matthew J. Wooller2, 3, Grant D. Zazula4, Elen Shute1, 5, A. Hope Jahren6, Pavel Kosintsev7, James A. Burns8, James Breen1, 9, Bastien Llamas1 and Alan Cooper1*

The role of environmental change in the late Pleistocene megafaunal extinctions remains a key question, owing in part to uncer-tainty about landscape changes at continental scales. We investigated the influence of environmental changes on megaherbi-vores using bone collagen nitrogen isotopes (n = 684, 63 new) as a proxy for moisture levels in the rangelands that sustained late Pleistocene grazers. An increase in landscape moisture in Europe, Siberia and the Americas during the Last Glacial–Interglacial Transition (LGIT; ~25–10 kyr bp) directly affected megaherbivore ecology on four continents, and was associated with a key period of population decline and extinction. In all regions, the period of greatest moisture coincided with regional deglaciation and pre-ceded the widespread formation of wetland environments. Moisture-driven environmental changes appear to have played an important part in the late Quaternary megafaunal extinctions through alteration of environments such as rangelands, which supported a large biomass of specialist grazers. On a continental scale, LGIT moisture changes manifested differently according to regional climate and geography, and the stable presence of grasslands surrounding the central forested belt of Africa during this period helps to explain why proportionally fewer African megafauna became extinct during the late Pleistocene.

The relative roles of human impacts and environmental change in the extinction of the late Pleistocene megafaunal communi-ties of Eurasia and the Americas during the LGIT, approxi-

mately 25–10 kyr bp (thousand years before present), remain poorly understood. Rapid warming events have recently been linked with megafaunal extinctions, both with and without the synergistic effects of human presence1,2. However, the apparently less severe impacts of previous glacial–interglacial cycles remain difficult to explain, and the potential role of human hunting is also compli-cated by the long-term coexistence of megafauna with early mod-ern human and Neanderthal hunters in both Africa and Eurasia. A crucial question in this debate is the nature and extent of the landscape changes during and after the Last Glacial Maximum (LGM, ~25–15 kyr bp) which accompanied extinction events in different parts of the world3,4. These changes can be caused by altered climate, human activities and the presence of megafauna, all of which exert strong top-down pressure on the vegetation, soil and nutrient cycling processes at the base of the food chain. These interactions can be detected through direct evidence (for example butchery marks on bones) or indirect methods such as relating the timing of climate events to extinction events in the palaeontologi-cal record1,3. Direct evidence approaches are particularly important in establishing causality, and studies of fossil abundance, body size and dietary stable isotopes have all identified environmental factors directly impacting megafaunal ecology5–7.

Megaherbivores played a critical role in Pleistocene ecosys-tems, functioning as ‘nutrient pumps’8, adapted to harvesting and distributing the nutrients available across widespread rangelands such as the mammoth steppe, a grass/forb-dominated biome extending from Europe to Siberia and across the Bering Land Bridge into northwestern North America4,9,10. Rangelands and grazers were interdependent, with megafauna impeding the colonization of trees through trampling and sapling consumption, while clearing and breaking down the organic matter from seasonally senescing foli-age9. Seasonal aridity also strongly influences the distribution of rangelands, such that climate change can exert extinction pressure on megafaunal rangeland grazers through habitat loss and range fragmentation9,11–13. Rangelands are particularly amenable to the study of moisture-driven environmental change through nitrogen isotopes preserved in grazer fossils, for which a strong inverse cor-relation exists between soil moisture and the heavier nitrogen stable isotope 15N relative to 14N (expressed using delta notation, δ 15N‰) in soils, plants and herbivore collagen14. The relationship is most pro-nounced where annual rainfall is lower than 500–700 mm (refs 14,15), which is typical for grassland biomes, and the probable range for those in the late glacial leading into the dry LGM16,17 (Supplementary Fig. 17, Supplementary References 5). Palaeoisotopic studies have detected LGIT decreases in δ 15N values across the mammoth steppe, which have been related to moisture from increasing precipitation and degrading permafrost5,12,16,18–20. The changes in δ 15N values

1Australian Centre for Ancient DNA, University of Adelaide, North Terrace, South Australia 5005, Australia. 2Alaska Stable Isotope Facility, Water and Environmental Research Center, Institute of Northern Engineering, University of Alaska Fairbanks, Fairbanks, Alaska 99775, USA. 3College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Fairbanks, Alaska 99775, USA. 4Yukon Palaeontology Program, Department of Tourism and Culture, Government of Yukon, Whitehorse, Yukon Y1A 2C6, Canada. 5School of Biological Sciences, Flinders University, Bedford Park, South Australia 5042, Australia. 6Centre for Earth Evolution and Dynamics, Department of Geosciences, University of Oslo, Postbox 1028, Blindern, N-0315 Oslo, Norway. 7Institute of Plant and Animal Ecology, Russian Academy of Sciences, 202 8 Marta Street, 620144 Ekaterinburg, Russia. 8Curator Emeritus, Quaternary Paleontology, Royal Alberta Museum, Edmonton, Alberta T5N 0M6, Canada. 9Robinson Institute, University of Adelaide, North Terrace, South Australia 5005, Australia. *e-mail: [email protected]; [email protected]


SUPPLEMENTARY INFORMATIONVOLUME: 1 | ARTICLE NUMBER: 0125

NATURE ECOLOGY & EVOLUTION | DOI: 10.1038/s41559-017-0125 | www.nature.com/natecolevol 1

http://dx.doi.org/10.1038/s41559-017-0125

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Contents

TableofContentsSupplementary:Methods.......................................................................................................................3

SMMethods1:Dataset..........................................................................................................................................3SMMethods2:DatahandlingandtheRMACalgorithm:motivation,description,implementation.........................................................................................................................................................4SMMethods3:Theeffectsofparametersr,a,b,c,m,ontheRMAC..................................................7

Supplementary:Text............................................................................................................................13SMText1:Proxychoiceandsourcesofvariationinthedata...........................................................13SMText2:Carbonstableisotopes.................................................................................................................15SMText3:Megafaunalcommunitycollapse.............................................................................................16SMText4:Non-graminivoreherbivoreδ15Nvalues..............................................................................17SMText5:SouthAmericanrangelandswereanalogoustothoseonthemammothsteppe18

Supplementary:FiguresandInvestigations.................................................................................18Supplementary:References...............................................................................................................27

SMReferences1:Datedδ15Nmeasurements............................................................................................27SMReferences2:Glaciationchronologies.................................................................................................28SMReferences3:PeatlandInitiationDates...............................................................................................28SMReferences4:Climaticfactors/proxies................................................................................................28SMReferences5:Selectedstudiesdemonstratingtheclimate-δ15Nlinkinmodernaridandsemi-aridenvironments.....................................................................................................................................28

Supplementary:Code(inRv3.2.2)..................................................................................................31Subroutines..............................................................................................................................................................31DataHandlingandCalibration........................................................................................................................32Insolation/PeatlandInitiation.........................................................................................................................34t-tests(SMFigures5)..........................................................................................................................................34

Supplementary:Bibliography...........................................................................................................35



SUPPLEMENTARY INFORMATION

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Supplementary:Methods

SMMethods1:Dataset

SMFigure1.Samplenumbersandlocations.

SMFigure1presentstheδ15Nmeasurementsusedinthisstudyalongwithglobalregion

andsource.Newmeasurements(n=63)arecreditedtoACAD(AustralianCentrefor

AncientDNA).OtherdatasourcesaredetailedinSMReferencesandSMBibliography.

UncalibrateddateswerecalibratedusingthecalibrationcurveIntCal13forallnorthern

hemispheredata,andSHCal13forSouthAmerica.Thecalibrationswereperformed

usingtheRpackageBchronandrepresentedasasinglevaluebytakingthemedianof

theprobabilitydistributionovertime(https://cran.r-project.org/package=Bchron;SM

Code).PreviouslypublishedcalibrateddateswereusedfromthestudiesindicatedinSM

Methods1.Indirectlydatedsamples(n=39BisonandEquussamplesfromDruckeret

al.,2003,andn=5sedimentsamplesfromHaileetal.,2009;seeSMData)wereonly

usedwhenmultiple14Cdatesfromadatedsedimentarylayerallfellwithin750yrsof

oneandanother,andcalibrationwasthenperformedusingthemeanofthe14Cdates

andthehighestofthe95%errormargins.Allsamplesdatingtobetween45and1ka

(thousandyearsbeforepresent)wereincludedinthemainstudyandtheSM

investigations.




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Samplesweregroupedbygenus.BisonandEquussamplesareabundantinthefossil

recordandtheirdietarypreferenceforgraminoidsreducesthepotentialeffectsofdiet

selectiononδ15Nvalues[1-4].EquusincludesspecimensidentifiedasE.caballusandE.

ferus,whileBisonincludesB.bison,B.bonasus,B.antiquus,B.priscus,alongwithsamples

identifiedonlytogenuslevel.LamacomprisesL.gracilisandL.guanicoe.FortheSM

analyses,MammuthusincludesonlyspecimensofM.primigeniuswhilethecarnivorous

felidsaredescribedinthetextaccompanyingSMFigure11.Similarly,sedimentδ15N

valuesaregiveninSMFiguresandInvestigations(Figure10).

Thesamplesusedinthemainstudy(Equus,Bison,andLama)werethengroupedinto

oneofthefollowingbins:European,Siberian(includingallEurasiansampleseastof

CentralEurope),NorthAmerican,orSouthAmerican(seeSMFigure1).TheNorthSlope

ofAlaskawasseparatedfromtheremainderoftheNorthAmericancontinentsince

ecologicalcontinuitybetweenthetwoisbrokenbytheBrooksRange,andthefossil-rich

NorthSlopeprovidesadequatedatatoindependentlycharacterisetrendsinδ15N

values.

SMMethods2:DatahandlingandtheRMACalgorithm:motivation,description,implementation

Wedevelopedanovelapproachtocombineallavailabledataineachregiontoyielda

proxyfortherelativechangesinMoistureAvailability(𝑀𝑀𝑀𝑀)overtime,basedongrazer

collagenδ15Nvalues.ThismethodproducesaRelativeMoistureAvailabilityCurve

(RMAC)basedonfivesteps:normalisation,exclusionofdata-poorregions,smoothing,

transformation,andscaling.Weaimedtoestimatechangesinrelativemoistureinaway

thataccountedappropriatelyforthereliabilityofthedataatdifferentpointsintime.To

achievethisweestablishedfourguidelines:1)Inperiodswheredataweretoosparseto

adequatelyrepresentδ15Nvariationthennoestimateofmoistureshouldbemade;2)

foranygiventimepoint,relativeMAshouldonlybeestimatedifdataoccurnearbyboth

beforeandafterthattimepoint;3)attimepointswherethedataaredense,itismore

reasonabletoreconstructrelativeMAbasedupontheabundantdataimmediately

surroundingthetimepoint.Wheredatabecomesparse,thefairestestimationof

relativeMAshouldbemoreheavilyinfluencedbymoredistantdata.4).Somemeasure




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ofthevariabilityoftherelativemoistureestimateatdifferentpointsintimeshouldbe

included.Guidelines1–3areillustratedinSMFigure2;

Tomakedatafromdifferenttaxacomparable,δ15Nvaluesarenormalisedseparatelyfor

eachgenusandtheresultingstandardscores(Z-scores)arethencombinedtogiveadataset𝒙𝒙=(𝑥𝑥!,𝑥𝑥!,...,𝑥𝑥𝑛𝑛),withndatapoints,meanequalto0,andstandard

deviationequalto1.Thisstepisintendedtoeliminatethedifferencesinmeanδ15N

values,aswellasheteroscedasticitybetweenregionsandtaxaduetoidiosyncraciesin

diet,habitat,andphysiology.Eachdatapointin𝒙𝒙isassociatedwithadate,listedin𝒕𝒕 =

𝑡𝑡/, 𝑛𝑛𝑡𝑡0, … , 𝑛𝑛𝑡𝑡2 .TheRMACreconstructionisbaseduponthecentraltendencyof𝒙𝒙over

time,inaccordancewiththepropertieslistedabove.Thesmoothingstep,asillustrated

below,accountsforguidelines1–3(seealsoSMMethods3).

SMFigure2.SchematicillustrationofthesmoothingmethodusedintheRMAC.Bluedots:data.Blackline:the

smoothedaveragethatislatertransformedtogivetheRMAC.Bluecurves:weightingfunctionsatselectedtime

points,illustratingtherelationshipbetweenthevarianceoftheweightingfunctionandthelocaldensityofdata

points.

Inpractice,thesmoothedvalueatatimepointisgivenbythefunction𝑠𝑠 𝑇𝑇 foreach

timepoint𝑇𝑇.Toaccountforguidelines1and2,sis𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢atanypointintime

wherefewerthan𝑚𝑚entriesin𝒕𝒕fallwithintherange 𝑇𝑇 − 𝑟𝑟, 𝑛𝑛𝑇𝑇 + 𝑟𝑟 ,orwhennoentries

in𝒕𝒕fallwithineither 𝑇𝑇 − 𝑟𝑟, 𝑇𝑇 or 𝑇𝑇, 𝑇𝑇 + 𝑟𝑟 ,forsomeconstant𝑟𝑟.Aflagvariable𝑢𝑢is




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giventhevalue1whentheseconditionsaremet,and0otherwise.Wherethese

conditionsarenotmet,𝑠𝑠(𝑇𝑇)isgivenastheweightedmeanofthesurrounding

datapoints,withthecontributionofeachpointweightedusingtheGaussianprobability

densityfunction𝐺𝐺(𝑥𝑥,µ,s).Inpractice,aGaussiankernelsmoother:

�𝑠𝑠 𝑇𝑇 =

𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢�if𝑢𝑢 = 1, and

𝐺𝐺 𝑇𝑇 − 𝑡𝑡F, 0,µG 𝒙𝒙𝒊𝒊�2�

FI/

𝐺𝐺 𝑇𝑇 − 𝑡𝑡F�, 0,µG2�FI/

otherwise.

ThevariableµG ,thestandarddeviationoftheweightingfunction,isincludedtoaccount

for guideline 3, declining in magnitude as data points become more sparse in the

vicinity of𝑇𝑇(see diagram above). To setµappropriately at each time𝑇𝑇, we require

some measure of the density of data in the vicinity of𝑇𝑇. We use the score𝑢𝑢G =

𝐺𝐺 𝑇𝑇 − 𝑡𝑡F�, 0, 𝑎𝑎 2�FI/ , which is proportional to the Gaussian kernel density at𝑇𝑇with

bandwidth𝑎𝑎. The standard deviation of the weighting function,µG , is then assigned

usingtherelationship𝑢𝑢G = 𝑏𝑏𝑢𝑢STUV ,whichallowsforthestandarddeviationtoapproach

zeroasthedensityincreases(seealsoSMMethods3).Theparameters𝑏𝑏and𝑐𝑐influence

howthedensityaffects theweighting function.Theparametersused in thisstudyare

�𝑎𝑎 = 1500, �𝑏𝑏 = 2000, 𝑐𝑐 = 100, 𝑟𝑟 = 3800,𝑚𝑚 = 3,withtheexceptionofEuropewhere𝑏𝑏

isset to10000, topreventoverfittingcausedbythehighdensityofdatapoints in this

group.

Smoothedvalues𝑠𝑠 𝑇𝑇 arethebasisofthelinesshownoverthedatadisplayedinthe

insetgraphsinMainTextFigure1.

ThedeterministiccomponentoftherelationshipbetweenmeasuresofMoisture

Availability(𝑀𝑀𝑀𝑀)andδ15Nvaluesinaridandsemi-aridenvironments(annual

precipitation<~500–800mm;seeSMReferences5andSMFigure14)hasbeen

describedasδ/]N~ `ab

+ 𝑧𝑧,where𝑦𝑦and𝑧𝑧areconstantsspecifictothesystemunder

study[5].Sincethereislittlesuchinformationaboutthepaleoecosystemsorspeciesin

thisstudy,andwearepoolingnormaliseddatafrommultipledifferentsystems(e.g.




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taxaandregions),wehaveassignedtherelative𝑀𝑀𝑀𝑀vs.δ15Nrelationshiptoprovidea

reasonablereconstructionoftherelativemoisturechangesovertime.Tomakeour𝑀𝑀𝑀𝑀

vs.δ/]Ncurvecompatiblewiththeresultsofempiricalstudies(seeSMReferences5)

weensuredthat,attheupperrangeofvalues,achangeinδ15Nvaluescorrespondstoa

changein𝑀𝑀𝑀𝑀perhapsfivetotentimessmallerthanasimilarchangeatthelowerendof

therange.Giventhevariationinpublishedstudiesacrossdifferentsystemsthis

approachseemsareasonablecompromiseuntilappropriateempiricaldataare

gathered.Thetransformationiseffectedusingthefunction:

𝑅𝑅 𝑇𝑇 =1

𝑠𝑠 𝑇𝑇 +𝑚𝑚𝑚𝑚𝑚𝑚GIfg],fff 𝑠𝑠 𝑇𝑇 + 1

.

Termsaddedto𝑠𝑠 𝑇𝑇 inthedenominatorupshiftthedatatoavoidundefinedvaluesof𝑅𝑅.

Theoutputof𝑅𝑅isinarbitraryunits,bestinterpretedasapproximatechangesinthe

relativeavailabilityofmoistureovertime,allowingthevaluestobescaledforplotting

andcomparisontootherproxies(e.g.MainTextFigure1).Foreachstudygroup,we

took1000bootstrapsamplesfromthedataandforeachevaluated𝑅𝑅at200points

spacedequallyalongtherange 0,45000 .Ateachpoint,abootstrap95thpercentile

confidenceintervaliscalculatedandplottedtoyieldtheRMACsshowninMainText

Figure1.

TheRMACreconstructionwasimplementedinRandthepackageggplot2(seeSM

Code).Deglaciationproxieswerereproduceddirectlyfromthesourcepublications

(listedinSMReferences2).PeatlandinitiationwasrepresentedbyapplyingR’sdensity

functionwithbandwidth1000onpeatlandinitiationdatesgroupedbyregion(North

Slopen=70,otherNorthAmerican=919,Siberian=363,Europen=518,South

American=54;sourceslistedinSMReferences3).Thepeatlandinitiationdateswere

calibratedusingthecalibrationcurvesandmethodsasdescribedfortheδ15Nvalues

(SMMethods1andSMCode).

SMMethods3:Theeffectsofparametersr,a,b,c,m,ontheRMAC




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ToillustratehowtheparametersoftheRMACreflectthereconstructionofrelative

moisture,weperformeddemonstrationsofthesmoothingstepofthealgorithmusinga

subsetoftheNorthAmericandata.

Inthecurrentstudy,theparameters(a,b,c,r,m)weresetas(1500,1500,100,3800,3).

Withtheseparameters,thesmoothedvaluesappearasfollows:

SMFigure3.TheRMACsmootherplottedoverNorthAmericandataandtheparameters(a,b,c,r,m)=

(1500,1500,100,3800,3).Themarkeddecreaseinrelativeδ15NcharacterisingtheLateGlacialNitrogenExcursionis

evidentat12–15ka.PointColours.Mammoth:cyan;Bison:red;Horse:green;Sediment:purple.

Theparameters𝑟𝑟and𝑚𝑚controltherequirementsforplotting.Withr=1000andm=6,

theRMAConlyplotsattimepointswhere6ormoredatapointsfallwithin1000yrsof

thetimepoint,withatleastonedatapointfallingoneachside:

SMFigure4.TheRMACsmootherplottedoverNorthAmericandata,withparametersasperSMFigure3,butwithr

=1000andm=6,causingthecurvetobeundefinedwheredataaresparse.PointColours.FollowsSMFigure3.

Z-scores,n

ormalised

δ15N(‰

)

Age(yearsbeforepresent)


Z-scores,n

ormalised

δ15N(‰

)




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Theparameter𝑎𝑎controlshowthedensityofdataismeasuredateachpointintime.A

lowvaluemeansthedensityisestimatedbasedlargelyonthedensityofdataveryclose

totheestimationpoint,whileahighervaluemeanstheestimategivesmoreweightto

datapointsfurtherfromtheestimationpoint.Ideally,thedensityscorewillchange

quicklywhereanobviouschangeindensityoccurs,suchasbeforeandafter~27kain

theexampleshere(SMFigures3–8).Theeffectofaltering𝑎𝑎canbeobservedbyplotting

thenormaliseddensityscores,toassessintuitivelyhowwelltheheightoftheline

reflectschangesinthedensityofdata.

Pale: Normaliseddensitywitha=5000

Medium: Normaliseddensitywitha=1500

Dark: Normaliseddensitywitha=500

SMFigure5.TheeffectofparameterauponRMACdensityscorescorrespondingtoNorthAmericandata,showing

howlowvaluesofaproducedensityscoresthataremoresensitivetochangesinthedensityofdata.Parameters

besidesaasperSMFigure3.LineColours.Dark:a=500;Medium:a=1500;Pale:a=5000.PointColours.Follows

SMFigure3.

Thedensityscore𝑑𝑑isusedtosetthestandarddeviationsoftheweightingdistributions

forthesmoother.AsshowninSMFigure2,thestandarddeviationissmallwherethe

dataaredense,andlargewherethedataaresparse.Therelationshipbetweenthe

kerneldensityandthestandarddeviationiscontrolledbytheparameters𝑏𝑏and𝑐𝑐:


Z-scores,n

ormalised

δ15N(‰

)

Densityscore(norm

alised)




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Red:NormalisedDensity(witha=1500)

Pale: Standarddeviationwithb=1500

Medium: Standarddeviationwithb=1000

Dark: Standarddeviationwithb=200

SMFigure6.TheeffectofparameterbupontherelationshipbetweenRMACdensityscores,andtheSDofthe

weightingfunctionusingtheNorthAmericandataset.ParametersbesidesbfollowSMFigure3.RedLine.RMAC

densityscore.BlueLines.SDofweightingdistribution.BlueLineColours.Dark:b=200;Medium:b=1000;Pale:b

=1500.

Red:NormalisedDensity

Pale: Standarddeviationwithc=50

Medium: Standarddeviationwithc=250

Dark: Standarddeviationwithc=500


SDofW

eigh

tingDistribu

tion

Density




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SMFigure7.TheeffectofparametercupontherelationshipbetweenRMACdensityscores,andtheSDofthe

weightingfunctionusingtheNorthAmericandataset.ParametersbesidescfollowSMFigure3.RedLine.RMAC

densityscore.BlueLines.SDofweightingdistribution.BlueLineColours.Dark:c=500;Medium:c=250;Pale:c=

50.

Todemonstratetheeffectofdensity-dependentweighting,theresultsofthesmoothing

stepcanbecomparedwithandwithoutthisfeature.TheredcurveinSMFigure8

representstheRMACsmootherwithdensity-dependentweighting(b=3000,c=300).

Thebluecurvesaredensity-independent(c=0)withnarrower(darkblue;b=1000)or

broader(lightblue;b=2000)weightingfunctions.Notethatthelightbluecurve

performswellinthelow-densityregions<27ka,butunderfitsinthedenseregions

around14–5ka.Thedarkbluecurveperformswellinthishigh-densityregionbut

overfitsinthelower-densityareas.TheredRMACsmootherappearstocapturethe

centraltrendswellinbothregions:

SMFigure8.AcomparisonoftheperformanceoftheRMACsmootherwithandwithoutdensity-dependent

weighting,usingNorthAmericandata,showinghowdensity-dependentweightingpreventstheunder-and


Density

SDofW

eigh

tingDistribu

tion


Z-scores,n

ormalised

δ15N(‰

)




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overfittingofthesmootherwithoutthisfeature.LineColours.Red:Smootherwithdensity-dependentweighting,(b

=3000,c=300);Darkblue:Smootherwithoutdensity-dependentweighting,withparameters(b=1000,c=0);

Lightblue:Smootherwithoutdensity-dependentweighting,withparameters(b=2000,c=0).PointColours.

FollowsSMFigure3.




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Supplementary:Text

SMText1:Proxychoiceandsourcesofvariationinthedata

SincethecurrentstudycomparesRMACsderivedfromdataspanningbroad

geographicalrangeswithproxiesfromasimilarlybroadrange,theresultsshouldbe

consideredanintegrationovercontinental/regional-scaletrends.Similarly,thetrends

aregeneralisedsincetheyrelyonageographicallyandtemporallyheterogeneous

sampleofrangelandfauna.However,sincethevariabledrivingthesignalisarguedto

bemoisture,andmoistureregimesactoverbroadspatialscales,somegeneralisation

oughttobepossible.Forexample,thevolumeofδ15Ndataappearsadequateto

demonstratethattheLateGlacialNitrogen-15Excursion(LGNE)fallswelloutsidethe

rangeofpre-LGM(LateGlacialMaximum)variation.Themagnitudeoftheδ15Nshift

andthecorrelationbetweendifferentregionsandtaxasuggestabiologicalevent

associatedwiththeclimaticandenvironmentaleventsoftheLastGlacial-Interglacial

Transition(LGIT;MainTextFig.2).

TheRMACreconstructionsarecontrastedwithdeglaciationproxiesforenvironmental

changeinMainTextFigure1.Whilethemassandareaoficesheetsisaproductof

complicateddynamicinputs(precipitation,condensation,surging)andoutputs

(melting,sublimation,calving),glaciationwasfavouredasaproxyforlargescale

climate-drivenchangesatthelandscapelevelbecausefactorscausinganicesheetto

retreatarealsolikelytobesignificanttoothersystems.Furthermore,theretreatofice

sheetsisitselfexpectedtostronglyinfluencethesurroundingenvironmentsvia

mechanismsincludingisostatic/eustaticsea-levelchange,thedischargeofmeltwater,

alteredwindandthermohalinecirculation,andtheretreatofperiglacialenvironments.

PeatlandformationisalsoplottedinMainTextFigure1asageneralisedproxyfor

environmentalchangeatcontinentalscalesovertimeduetotheextensivedatasets

availableforpeatlandinitiationoverabroadspreadoflocationswithinthestudy

regions,eachrepresentedbyasingledate(ratherthanaseriesofabundancemeasures

overtime).ThediscussionintheMainTextassumesthatgrazerspreferentially

aggregatedinflourishinggrasslandswheremoistureaccumulatedanddrovedownthe




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14

δ15Nvaluesofgraze.Thiswouldspecificallyrequireprolongedincreasedmoisture:in

aridgrasslands,theimmediateeffectofincreasedmoisture(forinstance,theanomalous

rainyseasonreportedbyArranibar,Otteretal.in2004[6])caninfactincreasefoliar

δ15Nvaluesintheshortterm.Therefore,δ15Nvaluesonlynormallydecreasealongwith

averagedlong-term(annual/decadal)moisturelevels.Peatlandformationhasthesame

requirementforpersistentmoistureinlocalisedareas,justinalargerdegreeandfora

longertime[7-9].

Theδ15Nvaluesofnaturalsamplescanbeinfluencedbyanumberofbiological,

geological,andchemicalprocesses.Forinstance,salinity,slope,pH,temperature,and

depthhaveallbeenlinkedtosoilandplantδ15Nvalues[10-13].Moisturelevelshave

predictableeffectsonseveralaspectsofthenitrogencycle.Ithasbeensuggestedthat

generallydryenvironmentshaveproportionallymoreinorganicnitrogen,whichis

moreamenableto15Nenrichmentviaevaporation,whilewetenvironmentshavemore

organicnitrogen,whichmaysuffergreater15Ndepletionvialeaching[14].Such

interactionsultimatelyleadtothestrongstatisticalδ15N-moisturecorrelationsthat

havebeenreportedfromfieldstudiesinaridandsemi-aridregions(seeSMReferences

5).Theabundanceofinfluencesonδ15Nvaluescancomplicatetheinterpretationof

palaeo-isotopicdata,howeveritislikelythatmoistureexertedtheprimaryinfluenceon

theassembleddatasetashasbeenobservedinmodernglobalstudiesofecosystemδ15N

values(SMReferences5).

TheLGITwarmingtrendisknowntohaveproducedlandscapemoisturethrough

severalmeans.Increasedprecipitationinmanyregionswascausedbychangesinglobal

windcurrentsandthermohalinecirculation,withstormtracksandlowpressure

regionsmovingmoistureinlandinEurope[15],Siberia[16],andNorthAmerica[17-

19],andtheInter-TropicalConvergenceZoneshiftingsouthwardoverSouthAmerica

[20].Highertemperaturesincreasedevaporation,andrisingsealevelsbroughtthe

influenceofmaritimeclimateregimesfurtherinland.Directevidenceforenhanced

LGNEmoistureinputareprovidedbyLGITrecordsofstreamincisiondetectedinthe

studyregionsofEurope[21,22],theNorthSlope[23],Alaska[24],Siberia[25,26],and

SouthAmerica[27,28].Thisisreinforcedbychangesincertainplantcommunities,

whicharemoredirectlylinkedtothesoilmoisturethatexertsstrongcontrolover




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15

grazerδ15Nvalues.LGITpollenprofilescollectedfromacrossthestudyregionsshow

transitionsofsteppevegetation(graminoid/forbdominance)toforestwithearly-

establishingtrees(oftendeciduousshrubsorsmalltrees),beingreplacedwithlarger

forestevergreens.Northofthetreelinethetransitionistotundravegetation,andwhere

thewaterbalanceallowsprolongedaccumulation,peatlandsform[20,29-32](seealso

SMReferences3).Withintheserobustgeneralrelationships,thecorrelationbetween

localproxiestothesoilmoisturethatinfluencesgraminivorecollagenδ15Nvaluesare

usuallyindirect,andheterogeneouswithinregionswithrespecttobothtimingand

extent.

SMText2:Carbonstableisotopes

Forthecollagensamplesanalysedforδ15Nvalues,wealsocomparedδ13Cvalueswith

datafrompreviousstudies(seeSMReferences1).Theδ13Cvaluesremainrelatively

stableduringtheLGNE,andthemoststrikingchangesareelevatedpost-LGNEδ13C

valuesinNorthAmericanbisonbetween~10and6ka,andaslightdecreasein

HoloceneEuropeanhorses(<10ka).Theseexcursionsareinoppositedirectionsand

arethereforelikelytobecontrolledbyvariablesactingatlocalscales[33].TheNorth

Americanδ13Cincreaseisfirstobservableinhorses,butbecomesmostapparentin

bisonsouthoftheicesheets(seeSMFigures1),andismostlikelytheresultofchanging

proportionsofC3vs.C4plantsinsouthernareas[34],thoughincreasingatmospheric

CO2mayalsohaveplayedapart[35].Theslightdecreaseinpost10kaEuropeanhorse

δ13Cvalues(~2permil)couldreflectadecreasedneedforWaterUseEfficiency(WUE)

byC3plants,changesinatmosphericCO2,oranincreasingcanopyeffect[36].Foliar

δ13CvaluesalsorespondstochangesinWUEinplants,whichmightwellbeexpectedto

alterwithchangesinmoisture.

Whileδ15Nvaluesaremostsensitiveunderaridconditions,δ13Cvaluesaremore

affectedbymoistenvironmentsandremainfarlesssensitivetochangesinmoisture

undercomparativelydryregimessuchasthoseknowntosustainextensiverangelands

inthePleistoceneandtoday[37].However,thelackofchangeinSiberianandNorth

Americanδ13CvaluesduringtheLGNEindicatesthatforagecompositionremained




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16

consistentwithaprimarilyC3,graminoiddietandrulesoutmajorshiftsinforage,e.g.in

favourofherbs[38].CurrentpalaeoecologicalevidencealsoindicatesthatC4plantsdid

notexpandintoBeringiaintheLatePleistocene(e.g.[39,40]).

Palaeo-isotopestudiesthatinvestigatefoodwebsgenerallyassumethatδ15Nina

speciesatagiventimeisstabilisedbythatspecies’tendencytoconsumethesamekinds

offorageacrossbroadgeographicranges[41].Toothwear,isotopicstudiesofplant

type,andthedietsofmodernanalogueshavebeenusedtoargueforthisstabilisation

[3,41].Overallthen,apurelydiet-basedexplanationfortheLGNEpatternseems

unlikely,andwouldneedtopositanear-simultaneoustransitionofmultiplegrazing

taxaondifferentcontinentstoanalternativefoodsourcethataffectedδ15Ninthesame

manner,andinallcaseswasnotreflectedinδ13Cvalues.Thisisunlikely,although

moderatechangesintheratioofplanttypesconsumedispossiblesincemoistureis

likelytoencouragetherangelandfloratoalterinsimilarways,forinstance,by

increasingtheproportionof15N-depletedforbsandpioneerrudderalsinthediet.

SMText3:Megafaunalcommunitycollapse

ItisimportanttoemphasisewhythePleistocenerangelandswerecriticalinthe

structureofthemegafaunalmammalcommunities.Akeyquestionaboutthis

environmentistheso-calledproductivityparadoxwhichaskshowanenvironmentthat

waspresumedtobelargelytundrawasabletosupportsuchalargebiomassof

mammals.Guthrie[3]hassuggestedthatrangelandswereinfactsomeofthemost

productiveenvironments,withawarmgrowingseasonthawprovidingamplemoisture

fordeep-rootedgrasses,whileclearskiesandlongdaysallowedforfastgrowthand

uptakeofmobilenutrients.Duringthecoldseason,deadsuperterraneanfoliagewas

rapidlycomposted,andthenutrientsredistributedbymegafauna.Cold-aridgrasslands

mayalsofavourlargegrazersinparticular:large-bodiedendothermicanimals,havinga

largesurfacearea-to-volumeratiosandthermalinertia,mayincuraselectivebenefitin

coldenvironments.Largebodiesalsoallowlargedigestivesystemsfortheextractionof

energyfromsizeableamountsoflow-qualityforagesuchasthegraminoidsthat

dominatearidregions.Also,largebodiesmayallowlongersurvivalwhenresourcesare




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17

scarce.Theselectiveadvantageconferredbythelattertwofactorsmaybeamplifiedby

strongseasonalshiftsinpolarregions[3].

Themaintextincludesonlycursorydiscussionofthemechanismsbywhichaltered

moistureregimesmayleadtoenhancedextinctionpressureonrangelandmegafauna,

focusingonhabitatlossasclimaticvariablesaltertheenvironment.Previousworkhas

exploredtheinterdependenceofmegafaunaandvegetation,alongwithotherfactors

suchasfire,andofferedinsightsintomechanismsthatdirectlylinkmegafaunal

extinctiontomoistureviathenitrogencycle[42-45].Forinstance,increasedmoisture

(alongsidetemperatureandatmosphericCO2)maycausenitrogentobecomealimiting

factorinthenutrientcycle.Herbivorebehaviourandphysiologychangessignificantlyto

compensateforinadequatefoliarnutrition[45],andbyretainingmorenitrogenand

focusingdeliberatelyonlocationscontaininghigh-nitrogenforage,theredistributionof

nutrientsbyherbivoresisimpaired.Thiscanfurtherexacerbatethenitrogenshortage

creatingafeedbackcycle[42].

SMText4:Non-graminivoreherbivoreδ15Nvalues.

Publishedstudies[46-49]havedemonstratedthatbrowsingdeerprobablyresponded

tosomeinfluencesnotaffectingtheobligatebrowsersusedtoinvestigatetherangeland

habitatsinthisstudy.Agradualincreaseinδ15NvaluesforNorthSlopecaribou

(Rangifertarandus)hasbeennotedfrom~40kaintotheLGM[48],whichisnot

obviouslyreflectedingrazervaluesfromtheregion.Whilethefacultativeconsumption

ofgrass,andsomesharedinfluencesofmoistureonbothgrazeandbrowseprobably

explainsthelateglacialdepletion(and,intheEuropeancaseofbothcaribouandred

deer,Cervuselephas,subsequentrecovery),theNorthSlopecaribourecordfromMann

et.al.(2013)[1]possiblyreflectscoolingandadecreaseinmoistureavailabilityleading

totheLGM,which,basedonhorse,bison,andmammothdatafromthesamestudy,

appearstohaveinfluencedtheecologyofrangelandslessthanthebiomesbrowsedby

caribou.Thepre-LGMincreasemaythereforereflectagreaterreliancebydeeron

graminoidmaterialincoldanddrytimes.Mostimportantly,however,thedecouplingof

theNorthSlopecaribouandgraminivorerecordsdemonstratesthatwhileecological




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18

changeswereoccurringbeforetheLGIT,theydidnotsignificantlyimpacttheentire

herbivorecommunityuntilthelateglacialmoistureincreases[48].

SMText5:SouthAmericanrangelandswereanalogoustothoseonthemammothsteppe

Landscape-scaleinterpretationsoflate-glacialenvironmentalchangepredictthata

similardecreaseinδ15Nvaluesshouldbefoundwhereveraridrangelandsgrazedby

mega-herbivoresweresubjecttoasustainedincreaseinplant-availablemoisture.

SouthernSouthAmericanrangelands,andtheirgraminivore[50]community,makea

goodanalogueforthemammothsteppe,sharingsimilarhistorieswithrespectto

changesintheenvironmentsandtheextinctions/rangeshiftsofthegrazers[28,51,52].

FollowingLGITpopulationdeclineandrangecontraction,Lamagracilisbecame

confinedtohighlandsteppesinthecentralwest,whileL.guanicoepopulationsalso

underwentrangeshiftsduringtheglacialperiod[53],andoccupiedsimilarsteppe-like

habitatsalongthecentralandsouthernwestcoast[54].Pleistocenerangelandswere

replacedbyvariousbiomesincludingwoodland,wetland,forest,anddesert,andthe

permafrostthatunderlaymuchofthecontinent’ssouthernhalfduringtheLGMisnow

onlyfoundathighaltitudesalongthewesterncordillera[28,55].Previousstudieshave

shownSouthAmericancamelidδ15Nvaluescorrelatewithprecipitationandtovary

overtime[56,57].Overallthen,thellamaisotopicsignalsrecordananalogouspattern

tothatseenforhorse,bison,andmammothacrosstheholarcticrangelands,suggesting

asimilarprocessofmoisturechangeassociatedwithmegafaunalextinctions.

Supplementary:FiguresandInvestigations

SMFigure9displaysallherbivoreδ15Ndatacompiledforthestudypriorto

normalisation.Mammothδ15NvalueswerenotusedinthemaintextRMAC

reconstruction,owingtotheirlowdensityandpoortemporalcoverage.WhiletheRMAC

ischangedverylittlebyinclusionofthesevalues,thenormalisationstephastheeffect

ofover-emphasisingnoiseinthedatawhenfewdatapointsareavailable.Wheredata

pointsoverlapwiththeLGNE,themammothδ15Nvaluesdoindeedsuggestadecline,

whichisexpectedgiventhatdigestivephysiology,dentalmorphology,modern

analogues[4],ancientDNA[58],andstableisotopeanalysis[1]indicatethatmammoth




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19

wererangelandgraminivoresthatsubsistedprimarilyonlargequantitiesoflow-quality

graminoidforagecomplementedbyforbs.

SMFigure9.Completeunnormalisedherbivoreδ15Ndatacompiledforthestudy,showingvaryingonsetofLGNE

patternindifferentregions.Totalsamplenumbersforeachregionaregivenineachpanel.NorthAmericansamples

areseparatedintothosefallinginEasternBeringia(Alaska,USA,andYukonTerritory,Canada;n=92),andsouthof

theNorthAmericanicesheetcomplex(AlbertaandBritishColumbiainCanada;Minnesota,Nevada,andWyomingin

theUSA;n=62).SiberiansamplesareseparatedintothosefromtheUralMountains(n=33)andotherregions

(includingtheRussianPlains,Taymyr,andWesternBeringia;n=56).SouthAmericanllamasareseparatedby

δ15 N

(‰)


n=231

n=154

n=152

n=89

n=45




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speciesintoLamagracilis(n=15)andL.guanicoe(n=30).Mammothδ15Nvaluesfromtheliterature(n=93;SeeSM

DataandSMBibliography)arealsoshown.

Modernfieldstudiesshowthatthemoisture-δ15Nrelationshipisdetectableattrophic

levelsfromsoilupwards.Ourmoisture-basedinterpretationofδ15Nchangestherefore

predictsthattheLGNEwillbereflectedinsoilδ15Nvalues.Inadditiontodatapresented

inthemaintext,wecollatedδ15Nmeasurementsfrompermafrostsedimentsamples(n

=5)fromariverembankmentatSteven’sVillage,Alaskatakenfromtwopointswithina

transectreportedbyHaileetal.,2009[59](SMFigure10).Theageofthesamplesis

estimatedtobeca.10.8and10.2karespectively,basedupontheradiocarbondates

madealongthetransect.Whilethesefewsoilmeasurementsrepresentonlya

preliminaryinvestigation,thelowδ15Nvaluesmatchthefaunalsignals,andare

consistentwiththereturnphaseoftheLGNE.Whilefoliarnitrogenmayshowa

depletionin15Ncomparedwithsoil[60,61],herbivoreδ15Nvaluesaretypically~3–

4‰higherthantheforagetheyconsume.Thesoilδ15Nvaluesappeartofallinarange

thatwouldbeunexpectedlylowiftheyhadnotexperiencedLGITdepletion.

SMFigure10.Sedimentδ15Ndata(n=4)fromSteven’sVillage,AlaskaNorthAmericajuxtaposedagainstherbivore

datafromthesameregion,showingthatvaluesconsistentwiththeLGNEcanbedetectedinsoilsdatingfromthe

sameperiod.

δ15 N

(‰)





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21

SMFigure11showstheraw(unnormalised)SouthAmericancamelidδ15Ndata,anda

similar,butelevated,patternofrapidincreaseinasetofcarnivoresfromthesamearea.

IthasbeenshownbyBumpetal.[33]thatorganismsathightrophiclevelsbetter

integratestableisotopesignaturesoverthesurroundingarea.Hencethecarnivoreδ15N

valuesindicatethatalargeproportionoftheherbivoresconsumedbythesefelids

(includingtaxaotherthanllamas)exhibitedthesamegeneraltrend,reinforcingthe

viewthattheeffectsofthelateglacialmoistureincreasewerelargeenoughtoaffectthe

megafaunalcommunitybeyondjustgrazers.Comparedtothellamas,thecarnivores

showδ15Nvalues3–4‰higher,consistentwiththeδ15Nvalueincreasethattypically

occursbetweenpredatorsandtheirprey.

SMFigure11:SouthAmericanunnormalisedδ15Ndataincludingcarnivores,showingarapidincreaseinvaluesfrom

~16kaconsistentwiththeLGNErecoveryphase,suggestingamajormoisturespikearoundtherapiddeglaciationof

~17–16ka.TheX-axishasbeenlimitedtobetween~15and17.5ka,toemphasisethestrongupwardδ15Ntrendat

thistime.DatafromSouthAmericanfelidcarnivores(n=13;Pumaconcolor,Smilodonpopulator,andPantheraonca)

demonstratethattheδ15Nsignalsweretransmittedtotheuppertrophiclevelsofthefoodchain.

δ15 N

(‰)





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22

SMFigure12:δ13Cvaluesfororiginaldatausedinthisstudyshowingtheoveralllackofsignificantchangesduring

theLGNEperiod.Thecausesandimplicationsofδ13Cchangesovertime(inparticularthosethatoccurafterthe

LGNE,suchasinNorthAmerica),isdiscussedinSMText2.

δ13 C(‰

)

Age(yearsbefore present)

n=231

n=154

n=152

n=89

n=45




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23

ThetestspresentedinSMFigures13–16aimtoinvestigatethestatisticalsignificanceof

changesinZ-normalisedδ15Nvalues.Dataweregroupedintopointsfallingwithina

particularwindow,andthosefallingoutside.Foreachwindow,aStudent’st-test(two-

tailed)wasusedtogenerateap-valueonthenullhypothesisthattheδ15Nvalueswithin

thewindowweredrawnfromthesamedistributionastheδ15Nvaluesoutsidethe

window.Eachfieldintheplotrepresentsawindow,withthecolourcorrespondingto

thep-value.Forinstance,thedarkestfieldintheEuropeanexamplebelow(SMFigure

13)representsawindowbeginningat11ka,andlastingfor3ky.Thisindicatesthatthe

period11–14kyacontainedδ15Nvaluesthatweremostsignificantlydifferentfrom

thosebeforeandafterthiswindow(p≤1E-50inthiscase).

Diagonalstripingisaconsequenceofδ15Nexcursions:longwindowscontainingthe

excursionmaystillhaveelevatedsignificancedespiteincludingsomedatapointsfrom

theregular(non-excursion)rangeofvalues.Thisisespeciallyprevalentwherethe

excursionabutsaperiodofsparseorabsentdata,forinstance,after10kaintheNorth

Slope(seeSMFigure9).Fieldsarecolouredgreywhenfewerthan5datapointsexist

withinthewindow.ThistestisnotinformativeontheSouthAmericadataset,which

showsastrong,singletrendinaveryshortperiod.

SMFigure13.Student’st-testsforsignificantvariationinz-normalisedδ15NvaluesinEurope,showinghighly

significantδ15Nexcursionsaround11–14ka.

window starts (ka)

Europe




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24

SMFigure14.Student’st-testsforsignificantδ15NvariationinNorthAmericashowinghighlysignificantδ15N

excursionsaround10–17ka.

SMFigure15.Student’st-testsforsignificantδ15NvariationontheAlaskanNorthSlope,showingahighlysignificant

δ15Nexcursionataround10ka.Notethediagonalstripingcausedbythelackofdataafter10ka.

100020003000400050006000700080009000

1000011000120001300014000150001600017000180001900020000

0 5000 10000 15000 20000 25000 30000 35000 40000 45000window begins (earliest) (kya)

win

dow

size

(ky) log(p−value)

−7.5

−5.0

−2.5

NorthSlope d15N z−scores (two−sample t−test, values in window vs other) log p−values

window starts (ka)

window starts (ka)

NorthSlope

NorthAmerica




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25

SMFigure16.Student’st-testsforsignificantδ15NvariationinSiberia,showingasignificantδ15Nexcursionaround

13–20ka.

Atlargegeographicscales,variationinmodernδ15Nvalueshasbeeninvestigatedwith

respecttomanyinter-correlatedvariables,suchasmoisture,temperature,altitude,and

latitude(SeeSMText1).Tojustifytheassumptionthattherelationshipbetweenδ15N

valuesandmoistureholdsbetweenrangelandenvironmentsindifferentcontinentswe

compareamodernδ15NvaluedatasetfromAustraliawithanotherfromAlaska,

demonstratingtheconsistencybetweentheresults.

SMFigures17.Investigationoftheglobalapplicabilityofthemoisture-δ15Nrelationship,usingmoderngrassdata(n

=35)collectedinAlaska[62],whichisoneofourstudyareas,andAustralia[5].ErrorbarsassociatedwiththeAlaska

window starts (ka)

Siberia




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26

δ15Nvalues=1standarddeviation.Thesetwogeographicallydisparateregionsshowcompatibleresults,withrespect

toboththedirectionoftherelationshipandtherawδ15Nvaluesatcertainmoisturelevels(approximatedhereusing

meanannualprecipitation).

Wechoseasubsetofthedatatoinvestigatewhetheranysourceofvariationinthedata

couldbeeasilyexplainedbyotherfactorsbesidesmoisture(SMFigures18–20).Bison

fromNorthAmerica(EasternBeringia,Canada,andMinnesota;n=17)werechosen

fromthepost-LGNE“stable”periodwheresomespreadinthedataarestillevident(5–

10ka).Itwasassumedthatpost-LGNEclimatemightbearbettercorrelationwith

modernclimatethandatadatingearlier,andonlyinlandsiteswereconsideredto

removethemajoreffectsofmarinetransgressionandcoastalclimateregimes.Theδ15N

valueswereplottedagainstpresent-daydecadalaveragesforannualtemperatureand

precipitationfornearbylocations(accessedviahttp://www.wrcc.dri.edu/cgi-

bin/cliMAIN.pl?mn5325and

http://www.eldoradocountyweather.com/canada/climate2/Calgary.html).Theresults

suggestthatmodernprecipitationisabetterpredictorforpalaeoδ15Nvariation

betweenregionsthantemperatureorlatitude,evensuggestingtheinverse-convex

relationshipseeninmodernstudiesofthiskind,especiallyinaridregions.

SMFigure18.Investigatingpossibleconfoundingfactorsintheclimate-δ15Nrelationship.Thisrepresentativesubset

ofthedatasuggestslatitudebearsnoobviousdirectrelationshiptoδ15Nvalues.

5678910

46 48 50 52 54lat

d15N

5678910

4 5 6 7temp_mu_c_year

d15N

δ15 N

(‰)

δ1

5 N(‰

)

Latitude(°N)

MAT(°C)




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27

SMFigure19.Investigatingpossibleconfoundingfactorsintheclimate-δ15Nrelationship.Thisrepresentativesubset

ofthedatasuggestsMeanAnnualTemperature(MAT)bearsnoobviousdirectrelationshiptoδ15Nvalues.

SM20.Investigatingclimate-δ15Nrelationships:MeanAnnualPrecipitation(MAP)appearstoobeytheinverse-convexrelationshiptoδ15Nseeninempiricalstudies.

Supplementary:References

SMReferences1:Datedδ15NmeasurementsPublicationssupplyingdataforthisstudyarelistedbyregionandtaxoninSMMethods.

ThedataareavailableinSupplementaryData.Thefollowinginstitutionsandcollectors

kindlysuppliedsamplesforthispublication,whichwegratefullyacknowledge:

USA:AmericanMuseumofNaturalHistory,NewYork,USA;UniversityofAlaska,

Fairbanks,USA;UniversityofKansasMuseumofNaturalHistory,Lawrence,KS,USA;

UniversityofAlaskaMuseumoftheNorth,USA;AlaskaDepartmentofFishandGame,

Fairbanks,USA;BellMuseumofNaturalHistory,UniversityofMinnesota,USA

Canada:CanadianMuseumofNature,Ottawa,Canada;CanadianMuseumof

Civilisation,Gatineau,Canada;ProvincialMuseumofAlberta/RoyalAlbertaMuseum,

Edmonton,Canada;SimonFraserUniversity,Vancouver,Canada;UniversityofVictoria,

Victoria,Canada;YukonHeritageCentre,Whitehorse,YT,Canada;YukonPalaeontology

Program,Whitehorse,YT,Canada;M.C.Wilson,privatecollection,Canada

Russia:InstituteofPlantandAnimalEcology,Ekaterinburg,Russia;Paleontological

Institute,Moscow,Russia;ZoologicalInstitute,St.Petersburg,Russia;Northern

ResearchStation,Cherskii,Russia;LaboratoryofPrehistory,StPetersburg,Russia;

LocalMuseum,Chersky,Russia

Europe:MalmoMuseumofNaturalHistory,Sweden;ZoologicalMuseumAmsterdam,

Netherlands:NaturalHistoryMuseum,London,UK

5678910

400 500 600precip_mu_mm_year

d15N

δ15 N

(‰)

MAP(mm)




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28

SouthAmerica:InstitutodelaPatagonia,UniversidaddeMagallanes,Magallanes,Chile;

LaPlataMuseum,UniversidadNacionaldeCuyo,Mendozo,Argentina;Museode

HistoriaNaturaldeSanRafael,Mendozo,Argentina;MuseodeHistoriaNatural,

UniversidadNacionalMayordeSanMarcos,Peru

SMReferences2:GlaciationchronologiesEurasianIceSheet:VandenBergetal.(2008)[63],Figure10;NorthernUrals:

Svendesenetal.(2014)[64],Figure14;NorthAmericanIceComplex:Tarasovetal.

(2003)[65],Figure18;PatagonianIceSheet:Boexetal.(2013)[66],Figure3.

SMReferences3:PeatlandInitiationDatesNorthernHemisphere(n=1870):[7-9];SouthAmerica(n=54):[9].

SMReferences4:Climaticfactors/proxiesNGRIP:[67];EPICADomeC:[68];Insolation:[69](http://CRAN.R-

project.org/package=palinsol).

SMReferences5:Selectedstudiesdemonstratingtheclimate-δ15Nlinkinmodernaridand

semi-aridenvironments

StudyTitle Reference Type Region Dataset

Convergenceofsoil

nitrogenisotopesacross

globalclimategradients

Craine2015[70] Global MultipleMixed,aridlesswell-

represented

Globalpatternsofthe

isotopiccompositionof

soilandplantnitrogen

Amundson2003[11] Global MultipleMixed,aridlesswell-

represented

Thecarbonandnitrogen

isotopecompositionof

Australiangrassesin

relationtoclimate

Murphy2009[5] Arid Australia Grass

Kangaroometabolism

doesnotcausethe

relationshipbetween

bonecollagenδ15Nand

wateravailability

Murphy2006[71] Arid Australia Grass,Kangaroo

Nitrogencyclinginthe

soil–plantsystemalonga

precipitationgradientin

Aranibar2004[6] Arid Africa Grass,Soil




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29

theKalaharisands

Effectsofclimateondeer

boneδ15Nandδ13C:

Lackofprecipitation

effectsonδ15Nfor

animalsconsuminglow

amountsofC3plants

Cormie1996[72]Semi-arid–

MesicNorthAmerica Deer

15NEnrichmentinthe

Sahara:InSearchofa

GlobalRelationship

Schwarcz1999[73]Arid–Semi-arid,

compilationAfrica Human,Animal,Plants

Altitudinalgradientsof

grasslandcarbon

andnitrogenisotope

compositionare

recordedinthehairof

grazers

Mannel2007[74]Semi-arid–

MesicEurope

Sheep,Cattle,Goats,

Alpaca,Horse

Quaternaryrecordof

aridityandmeanannual

precipitation

basedonδ15Ninratite

anddromornithid

eggshellsfromLakeEyre,

Australia

Newsome2011[75] Arid–Semi-arid Australia MoaEggshell,Grass

StableNitrogenIsotope

ValuesinArid-Land

KangaroosCorrelated

withMeanAnnual

Rainfall:Potentialasa

PalaeoclimaticIndicator

Pate2008[76] Arid–Semi-arid Australia Kangaroo

Annualrainfalland

nitrogen-isotope

correlationinmacropod

collagen:applicationasa

paleoprecipitation

indicator

Grocke1997[77] Arid,Coastal Australia Possum,Kangaroo

The15Nnatural

abundance(δ15N)of

ecosystemsamples

reflectsmeasuresof

wateravailability

Handley1999[14] Global Multiple

Soil,Plant.Arid

reasonablywell-

represented.

Climaticinfluenceonthe Heaton1986[78] Arid Africa Human,Elephant,




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30

isotopiccompositionof

bonenitrogen

Wildebeest,Giraffe,

Zebra

Nitrogenisotopicecology

insouthernAfricaSealy1987[79] Arid Africa

ManyAfricanAnimals

(groupedbycollection

site)

Plantstableisotope

compositionacross

habitat

gradientsinasemi-arid

savanna:implicationsfor

environmental

reconstruction

Codron2007[80] Arid–Semi-arid Africa Grass,Trees

Conservationofnitrogen

increaseswith

precipitationacrossa

majorgrasslandgradient

intheCentralGreatPlains

ofNorthAmerica

McCulley2008[81]Semi-arid–

MesicNorthAmerica Grass

Naturalabundanceof13C

and15NinC3andC4

vegetationofsouthern

Africa:patternsand

implications

Swap2003[82] Arid–Semi-arid Africa Plants

Patternsandimplications

ofplant-soilδ13Candδ15NvaluesinAfrican

savannaecosystems.

Wang2010[83] Arid–Semi-arid Africa Plants




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Supplementary:Code(inRv3.2.2)

Subroutines#z-transformavectorztransform<-function(data){mu<-mean(data)sd<-sd(data)(data-mu)/sd}#residualsfromthemean.notusedinfinalpaperbutpossiblyuseful.residuals<-function(data){mu<-mean(data)data-mu}#weightingvaluesbasedontheGaussian.Scaledtobetween0and1.gaussian_weights<-function(calc_point,data_points,stdev){dnorm(data_points,mean=calc_point,sd=stdev)/dnorm(0,0,sd=stdev)}#z-transformsbylevelsofafactor.Forbigdatasets,rewritingthisusingapply-familyfunctionswillbeessential.factorwise_ztransform<-function(x,values_colname,factor_colname){for(levinlevels(x[[factor_colname]])){x[values_colname][x[factor_colname]==lev&!is.na(x[factor_colname])&!is.na(x[values_colname])]<-ztransform(x[values_colname][x[factor_colname]==lev&!is.na(x[factor_colname])&!is.na(x[values_colname])])}out<-x[values_colname]colnames(out)<-paste(sep="",values_colname,"_Z_trans_",factor_colname,"wise")out}#notusedinfinalpaperbutpossiblyusefulfactorwise_residuals<-function(x,values_colname,factor_colname){for(levinlevels(x[[factor_colname]])){#print(lev)#print(x[values_colname][x[factor_colname]==lev&!is.na(x[factor_colname])&!is.na(x[values_colname])])x[values_colname][x[factor_colname]==lev&!is.na(x[factor_colname])&!is.na(x[values_colname])]<-residuals(x[values_colname][x[factor_colname]==lev&!is.na(x[factor_colname])&!is.na(x[values_colname])])}out<-x[values_colname]colnames(out)<-paste(sep="",values_colname,"_residuals_",factor_colname,"wise")out}#giveitavectorofdatapointsbootstrap<-function(x){x[floor(runif(nrow(x))*nrow(x)+1),]}RMAC_unscaled<-function(x,df,dd_sd,sd_a,sd_b,top,bottom,...){df<-df[!is.na(df[,1])&!is.na(df[,2]),]#cleanNAsfromdfif(!missing(...)){df<-df[multimatch(df[,3],list(...)),]}if(sum(x<=0)>0){df[,1]<--df[,1]}#reversingthex-axisjustfeedsstat_functionthex-valuesasnegative...sowedealwithitlikethisdf[,2]-min(df[,2])+1000000->df[,2]#thisisarelativescaleandit'sjusteasiertoworkwithpositivevalueshereratherthanmessingwiththefunction.theresultisexactlythesame.outvector<-numeric()#defineoutputstoragefor(tinx)#tisthetimeatwhichwewanttoknowtheRMAC_unscaled{num<-0#numeratorden<-0#denominatorl_in<-0#numberwithin2sdofmeanonleftr_in<-0#onright#forthisx(akap),scanthroughtime/d15Npairs,applycontributiontonumerator(Ndensity(p-time,0,sd)*d15N)anddenominator(Ndensity(p-time,0,sd))#print("t");print(t)




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densityscore<-0for(rowin1:nrow(df))#foreachdatapoint(time,measurement)pair{dist<-t-df[row,1]#distanceofdatapointfromtif((dist>=0)&(dist<=2*dd_sd)){l_in<-l_in+1}#l_in:pointstothe(l)eftoftthatare(in)thethresholdelseif((dist<=0)&(dist>=-2*dd_sd)){r_in<-r_in+1}densityscore<-densityscore+dnorm(dist,0,dd_sd)#ascoretocharacterisehowdensethedatapointsarearoundthist.}#print(paste("lin/rin",l_in,"/",r_in))#print(paste("densityscore:",densityscore))sd_wt<-sd_a*exp(-sd_b*densityscore)#defineanappropriatesdfortheweightingdistribution#print(c("sdthiscalcpoint:",sd_wt))#reallyusefulforchoosingmeaningfulparamsfor(rowin1:nrow(df))#foreachdatapoint(time,measurement)pair{dist=t-df[row,1]#distofpointfromtweight<-dnorm(dist,0,sd_wt)#usingthedefinedweightingdistnum<-num+(weight*df[row,2])den<-den+weight;}mu<-numeric()if((!(l_in>0)||!(r_in>0))||(l_in+r_in<6)){mu<-NA}#limitsbasedonlinandrinelse{mu<-1/(num/den)}#mu<-1/(num/den)#nolimitsoutvector<-c(outvector,mu)}#print(outvector)#(((outvector-min(outvector,na.rm=T))/(max(outvector,na.rm=T)-min(outvector,na.rm=T)))*(top-bottom))+bottom->outvector#toscalebetweentopandbottomreturn(outvector)}#returnsthemeanor95%CIsateachtime.“What”canbe“mu”,“upper”or“lower”.RMAC_unscaled_bstrap_intervals<-function(x,df,dd_sd,sd_a,sd_b,top,bottom,what,...){n_bootstraps<-1500 x->timesdo.call(rbind,lapply(1:n_bootstraps,function(x)RMAC_unscaled(times,bootstrap(df),dd_sd,sd_a,sd_b,top,bottom,...)))->bstraps#rescalehere(bstraps-min(bstraps,na.rm=T))/(max(bstraps,na.rm=T)-min(bstraps,na.rm=T))*(top-bottom)+bottom->bstrapsreturn(do.call(rbind,apply(bstraps,2,function(x){strap<-x[!is.na(x)]n_out_each_end<-round(((.05*length(strap))/2),digits=0)if(n_out_each_end>0){return(data.frame('mu'=median(strap),'upper'=strap[order(strap)][(length(strap)-n_out_each_end)],'lower'=strap[order(strap)][n_out_each_end+1]))}else{return(data.frame('mu'=NA,'upper'=NA,'lower'=NA))}}))[,what]#delete[,what]togetthewholedataframe,i.e.whatisplottedinthemsfig1.)}

DataHandlingandCalibrationThecolumn“notes”herewasusedasaconvenientinformalrepositoryofrelevantmetadataconcatenatedintoas-long-as-necessarycharacterstringsandusedtogrouporextractinformationforsamples.Itisnotincludedinthesupplementarydatabutisavailableuponrequest.x<-read.csv(file="data.csv")#columnsnamed"study","genus","region","date","cal_error","d13C","d15N,"dating_record","notes","locality","extra","diet","d15N_Z_trans_genuswise","cal_date"as.character(x$notes)->x$notesx$extra[x$extra==""]<-NAas.character(x$dating_record)->x$dating_recordas.character(x$locality)->x$localityas.numeric(sub("?","-",x$d15N,fixed=T))->x$d15Nas.numeric(sub("?","-",x$d13C,fixed=T))->x$d13Cas.numeric(sub(",","",x$date,fixed=T))->x$date




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as.numeric(as.character(x$d15N))->x$d15N#addedstuffforexplorationlevels(x$extra)<-c(levels(x$extra),"EB","NonEBCan","USA")x$extra[x$region=="NorthAmerica"&grepl("hicken|ukon|YT|laska|AK|IrishGulch|Quartz",x$notes)]<-"EB"x$extra[x$region=="NorthAmerica"&grepl("lberta|AB|olumbia|dmonton|BC|Charli|Buffalo",x$notes)]<-"NonEBCan"x$extra[x$region=="NorthAmerica"&grepl("hicken|ukon|YT|laska|AK|innesota|Trap|48States",x$notes)]<-"USA"x$extra[x$region=="NorthAmerica"&grepl("azula|obbs|zpak",x$study)]<-"EB"#separatellamasbyspecieslevels(x$extra)<-c(levels(x$extra),"guanaco","gracilis","unidentified")x$extra[x$genus=="Lama"&grepl("ExcinctG",x$extra)]<-"guanaco"x$extra[x$genus=="Lama"&grepl("Gracilis",x$extra)]<-"gracilis"x$extra[x$genus=="Lama"&grepl("Holocene",x$extra)]<-"gracilis"x$extra[x$genus=="Lama"&grepl("UnID",x$extra)]<-"unidentified"x$extra[x$genus=="Lama"&is.na(x$extra)]<-"gracilis"droplevels(x[!grepl("USA",x$extra)&!grepl("unidentified",x$extra),])->x#addsiberiaseparationlevels(x$extra)<-c(levels(x$extra),"Urals","Other")x$extra[grepl("rals",x$notes)&x$region=="Siberia"]<-"Urals"x$extra[!grepl("rals",x$notes)&x$region=="Siberia"]<-"Other"#doz-transformationsdroplevels(x)->xrm(b,t)for(iinlevels(x$region)){x[x$region==i,]->tcbind(t,factorwise_ztransform(t,"d15N","genus"))->tif(!exists("b")){t->b}else{rbind(b,t)->b}}b->xrm(b,t)x<-x[x$date<45000&x$date>1000&x$d15N_Z_trans_genuswise>-3&x$d15N_Z_trans_genuswise<3&1,]x<-x[!is.na(x$date)&!is.na(x$d15N)&1,]x<-x[x$genus!="Mammuthus",]#calibratex$cal_date=x$datelibrary(Bchron)length(x$date[!is.na(x$date)&!is.na(x$cal_error)])->lcalcurves<-ifelse(x$region[!is.na(x$date)&!is.na(x$cal_error)]=="SouthAmerica","shcal13","intcal13")BchronCalibrate(ages=x$date[!is.na(x$date)&!is.na(x$cal_error)],ageSds=x$cal_err[!is.na(x$date)&!is.na(x$cal_error)],calCurves=calcurves)->calsx$cal_date[!is.na(x$date)&!is.na(x$cal_error)]<-as.numeric((lapply(cals,function(i){range(i$ageGrid)[1]+(sum(i$densities)/2)*(range(i$ageGrid)[2]-range(i$ageGrid)[1])})))droplevels(x)->x#tidyupandhavealookstr(x)table(x$genus,x$region)table(x$study,x$region)




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Insolation/PeatlandInitiation#insolationlibrary(palinsol)insolation<-function(times,astrosol=la04,ltd,...)sapply(times,function(tt)Insol(orbit=astrosol(tt),long=ltd,lat=65*pi/180))tts<-seq(from=-45000,to=0,by=100)isl_S<-insolation(tts,ber78,ltd=pi/2)isl_W<-insolation(tts,ber78,ltd=pi/2)plot(tts,isl,typ='l')ggplot(data=data.frame(insol_S=isl_S,insol_W=isl_W,time=-tts))+geom_line(mapping=aes(x=time,y=insol_S))+geom_line(mapping=aes(x=time,y=insol_W))#peatlands#readinread.csv(file="data.csv")[,1:7]->x#columnsinclude“lat”,“long”,“date”,”region”#alittlecleaningsub(",","",x$date)->x$date#assignregionsbylatitudec(levels(x$region),"Siberia","Europe","NA","NorthSlope")->levels(x$region)cut(x$long[x$ref=="MacDonnell_06"],breaks=c(-180,-169.02,-54.99,-13.9,58.68,180),labels=c("Siberia","NorthAmerica","NA","Europe","Siberia"))->x$region[x$ref=="MacDonnell_06"]x$region[x$lat>68.26&x$long>-166.49&x$long<-140.96]<-"NorthSlope"x<-droplevels(x)x$date<-as.numeric(as.character(x$date))

t-tests(SMFigure13–16)#dop-valuesovertimefunction(x=timepoint,df=dataframe,willgiveyoup-valueusingt-testforalld15Nvalspre-pointvs.afterpoint)pval_split<-function(from,to,df)#requirescolumnsofdataframe“df”labeled“date”and“d15N_Z_trans_genuswise”.{#print(paste("to:",to,",from:",from))if(from>45000){return(NA)}#windowextendsbeyondendif(to>=from){return(NA)}#windowbeyond<-df$d15N_Z_trans_genuswise[(df$date>from|df$date<to)&!is.na(df$d15N_Z_trans_genuswise)]#before+afterwithin<-df$d15N_Z_trans_genuswise[df$date<from&df$date>to&!is.na(df$d15N_Z_trans_genuswise)]#betweenif(length(beyond)<5||length(within)<5){return(NA)}t.test(x=beyond,y=within)[["p.value"]]}#makematrix#eachcellwillbethep-valuewhenexcludingtheregion[from,to]inc<-1000loc<-"NorthSlope"#makem.adataframewhererowsarethestartpointsofeachwindow,andcolsarethewinsizem<-sapply(seq(1000,20000,by=1000),function(winsize)sapply(seq(0,45000,by=inc),function(a)pval=pval_split(a+winsize,a,x[x$region==loc,])))m<-as.data.frame(m)dimnames(m)<-list(seq(0,45000,by=inc),seq(1000,20000,by=1000))df<-data.frame()for(iincolnames(m)){for(jinrownames(m)){df<-rbind(df,data.frame(t=j,w=i,p=m[j,i]))}}#atypicalplotlibrary(ggplot2)ggplot(data=df,aes(x=t,y=w,fill=log(p)))+geom_tile()+scale_fill_gradient(low="steelblue",high="white",guide=guide_legend(title="log(p-value)"))+scale_x_discrete(breaks=c(seq(0,45000,5000)))+xlab("windowbegins(earliest)(kya)")+ylab("windowsize(ky)")+ggtitle(paste(loc,"d15Nz-scores(two-samplet-test,valuesinwindowvsother)logp-values"))




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